Evaluation based on observer studies is time consuming and costly and optimization is just not viable. There is therefore a strong rationale for image quality measures with the predictive accuracy of observer studies but that are time and cost efficient. We build on our previous work on computer model observers to extend the application of model observers from basic detection tasks where the lesion is invariant (detection/signal- known exactly on D-SKE tasks) to more realist tasks where the lesion appearing in the image can be of different types and sizes (detection- classification/signal known statistically or DC-SKS tasks). Our goal is to develop a computer model observer that can be used to predict the effect of image compression in these more realistic detection/classification tasks with lesion variability. This model could be used for optimization of compression algorithm parameters with respect to task performance in these more realistic tasks. To achieve this goal we propose five specific aims: 1) To develop computer model observers for tasks where the observers have to detect and classify a lesion and where the lesions have different sizes and/or orientations. 2) To perform psychophysical measurements of five different state of the art image compression algorithms (some of them which are being considered as the JPEG 2000 international standard) in the DC-SKS task and compare it to our previous results with D-SKE tasks. 3) To compare the newly proposed DC-SKS model observers with respect to their ability to predict the observer task performance measured in specific aim 2. 4) To use the DC-SKS model observer with highest predictive power to perform automated optimization of compression algorithms using stimulated annealing techniques. 5) To perform psychophysical comparisons of the effects of the compression algorithms on tasks with the default compression parameters, DCS-SKS model optimized parameters, and the more traditional D-SKE model optimized parameters. If successful, we will extend the use of model observers to more realistic tasks. The impact of this research will be improved computer based metrics for cost and time efficient evaluation of medical image quality as well as more rapid and cost effective communication and storage of digital coronary angiograms.